
'''simple docstring'''


import numpy as np

import datasets


snake_case_			:	str 	=       '\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.\nIt was introduced by Prof. P. C. Mahalanobis in 1936\nand has been used in various statistical applications ever since\n[source: https://www.machinelearningplus.com/statistics/mahalanobis-distance/]\n'

snake_case_			:	List[str] 	=       '\\n@article{de2000mahalanobis,\n  title={The mahalanobis distance},\n  author={De Maesschalck, Roy and Jouan-Rimbaud, Delphine and Massart, D{\'e}sir{\'e} L},\n  journal={Chemometrics and intelligent laboratory systems},\n  volume={50},\n  number={1},\n  pages={1--18},\n  year={2000},\n  publisher={Elsevier}\n}\n'

snake_case_			:	Any 	=       '\nArgs:\n    X: List of datapoints to be compared with the `reference_distribution`.\n    reference_distribution: List of datapoints from the reference distribution we want to compare to.\nReturns:\n    mahalanobis: The Mahalonobis distance for each datapoint in `X`.\nExamples:\n\n    >>> mahalanobis_metric = datasets.load_metric("mahalanobis")\n    >>> results = mahalanobis_metric.compute(reference_distribution=[[0, 1], [1, 0]], X=[[0, 1]])\n    >>> print(results)\n    {\'mahalanobis\': array([0.5])}\n'


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,       _KWARGS_DESCRIPTION )
class     lowercase__						(	datasets.Metric ):






   '''simple docstring'''







   def    UpperCAmelCase					(      self ):

      '''simple docstring'''







      return datasets.MetricInfo(
          description=_DESCRIPTION			, citation=_CITATION			, inputs_description=_KWARGS_DESCRIPTION			, features=datasets.Features(
              {
                  '''X''': datasets.Sequence(datasets.Value('''float'''			, id='''sequence''' )			, id='''X''' ),
              } )			, )







   def    UpperCAmelCase					(      self			, lowerCamelCase__			, lowerCamelCase__ ):

      '''simple docstring'''







      UpperCamelCase	  =	np.array(_A )
      UpperCamelCase	  =	np.array(_A )

      # Assert that arrays are 2D
      if len(X.shape ) != 2:
         raise ValueError('''Expected `X` to be a 2D vector''' )
      if len(reference_distribution.shape ) != 2:
         raise ValueError('''Expected `reference_distribution` to be a 2D vector''' )
      if reference_distribution.shape[0] < 2:
         raise ValueError(
             '''Expected `reference_distribution` to be a 2D vector with more than one element in the first dimension''' )

      # Get mahalanobis distance for each prediction
      UpperCamelCase	  =	X - np.mean(_A )
      UpperCamelCase	  =	np.cov(reference_distribution.T )
      try:
         UpperCamelCase	  =	np.linalg.inv(_A )
      except np.linalg.LinAlgError:
         UpperCamelCase	  =	np.linalg.pinv(_A )
      UpperCamelCase	  =	np.dot(_A			, _A )
      UpperCamelCase	  =	np.dot(_A			, X_minus_mu.T ).diagonal()

      return {"mahalanobis": mahal_dist}













'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available


_UpperCamelCase						:						Tuple  =					{
    'configuration_audio_spectrogram_transformer': [
        'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ASTConfig',
    ]
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_UpperCamelCase						:						Optional[int]  =					[
						    'AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'ASTForAudioClassification',
						    'ASTModel',
						    'ASTPreTrainedModel',
						]

try:
						if not is_speech_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_UpperCamelCase						:						int  =					['ASTFeatureExtractor']

if TYPE_CHECKING:
						from .configuration_audio_spectrogram_transformer import (
						    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    ASTConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_audio_spectrogram_transformer import (
												    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ASTForAudioClassification,
												    ASTModel,
												    ASTPreTrainedModel,
												)

						try:
												if not is_speech_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
						import sys

						_UpperCamelCase						:						List[str]  =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


